Evidence map›Paper›PMID 40402288›Full record

ReviewEuropean radiology2025

ESR Essentials: a step-by-step guide of segmentation for radiologists-practice recommendations by the European Society of Medical Imaging Informatics.

Kalina Chupetlovska, Tugba Akinci D'Antonoli, Zuhir Bodalal, Mohamed A Abdelatty, Hendrik Erenstein, João Santinha, Merel Huisman, Jacob J Visser, Stefano Trebeschi, Kevin B W Groot Lipman

Abstract readReview
In one paragraph

Review in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Kalina ChupetlovskaDepartment of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Tugba Akinci D'AntonoliDepartment of Diagnostic and Interventional Neuroradiology, University Hospital Basel, Basel, Switzerland.
Zuhir BodalalDepartment of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Mohamed A AbdelattyDepartment of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Hendrik ErensteinDepartment of Medical Imaging and Radiation Therapy, Hanze University of Applied Sciences, Groningen, The Netherlands.
João SantinhaDigital Surgery LAB, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Merel HuismanDepartment of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
Jacob J VisserDepartment of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.
Stefano Trebeschi *Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Kevin B W Groot Lipman *Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands. k.groot.lipman@nki.nl.ORCID http://orcid.org/0000-0003-3651-2529

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-quality segmentation is important for AI-driven radiological research and clinical practice, with the potential to play an even more prominent role in the future. As medical imaging advances, accurately segmenting anatomical and pathological structures is increasingly used to obtain quantitative data and valuable insights. Segmentation and volumetric analysis could enable more precise diagnosis, treatment planning, and patient monitoring. These guidelines aim to improve segmentation accuracy and consistency, allowing for better decision-making in both research and clinical environments. Practical advice on planning and organization is provided, focusing on quality, precision, and communication among clinical teams. Additionally, tips and strategies for improving segmentation practices in radiology and radiation oncology are discussed, as are potential pitfalls to avoid. KEY POINTS: As AI continues to advance, volumetry will become more integrated into clinical practice, making it essential for radiologists to stay informed about its applications in diagnosis and treatment planning. There is a significant lack of practical guidelines and resources tailored specifically for radiologists on technical topics like segmentation and volumetric analysis. Establishing clear rules and best practices for segmentation can streamline volumetric assessment in clinical settings, making it easier to manage and leading to more accurate decision-making for patient care.

Indexed as

Diagnostic ImagingMedical InformaticsRadiologyEuropeHumansPractice Guidelines as TopicRadiologistsSocieties, MedicalArtificial intelligenceImagingSegmentationVolumetry

Identifiers

PMID40402288
PMCPMC12559127

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.